This book explores multivariate statistics from traditional and modern perspectives. It covers core topics like multivariate normality, MANOVA, and canonical correlation analysis, as well as modern concepts such as gradient boosting, random forests,...Loe edasi...
In medical and social science research, MGLMMs help disentangle state dependence from incidental parameters. Focusing on these sophisticated data analysis techniques, this work presents robust and methodologically sound models for analyzing large an...Loe edasi...
This book introduces artificial neural networks to students and professionals. It covers the theory and applications in statistical learning methods with concrete Python code examples....Loe edasi...
Complex Survey Data Analysis with SAS® is an invaluable resource for applied researchers analyzing data generated from a sample design involving any combination of stratification, clustering, unequal weights, or finite population correction factors....Loe edasi...
This book provides a thorough literature review on Multivariate Process Capability Indices. It discusses the various aspects and methodologies associated with the subject in one place. Several real-life data sets have also been used to show the appl...Loe edasi...
This book provides practical guidance on multiple imputation analysis, from simple to complex problems using real and simulated data sets. Data sets from cross-sectional, retrospective, prospective and longitudinal studies, randomized clinical trial...Loe edasi...
Contains chapters based on presentations given at the 33rd Carnegie Symposium on Cognition. This book offers a presentation of the research on thinking with data. It focuses on the concepts of uncertainty and variation and on how people understand t...Loe edasi...
This text presents multivariate statistical methods, accompanied by examples relevant to students in marketing and business concentrations, making extensive use of the SAS package of statistical programs....Loe edasi...
